Innovations in Bayesian Networks pp 131-167

Part of the Studies in Computational Intelligence book series (SCI, volume 156) | Cite as

Objective Bayesian Nets for Systems Modelling and Prognosis in Breast Cancer

  • Sylvia Nagl
  • Matt Williams
  • Jon Williamson

Abstract

Cancer treatment decisions should be based on all available evidence. But this evidence is complex and varied: it includes not only the patient’s symptoms and expert knowledge of the relevant causal processes, but also clinical databases relating to past patients, databases of observations made at the molecular level, and evidence encapsulated in scientific papers and medical informatics systems. Objective Bayesian nets offer a principled path to knowledge integration, and we show in this chapter how they can be applied to integrate various kinds of evidence in the cancer domain. This is important from the systems biology perspective, which needs to integrate data that concern different levels of analysis, and is also important from the point of view of medical informatics.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Abramovitz, M., Leyland-Jones, B.: A systems approach to clinical oncology: Focus on breast cancer. BMC Proteome Science 4, 5 (2006)CrossRefGoogle Scholar
  2. Al-Kuraya, K., Schraml, P., Torhorst, J., Tapia, C., Zaharieva, B., Novotny, H., Spichtin, H., Maurer, R., Mirlacher, M., Kochl, O., Zuber, M., Dieterich H., Mross, F., Wilber, K., Simon, R., Sauter, G.: Prognostic relevance of gene amplifications and coamplifications in breast cancer. Cancer Research 64, 8534–8540 (2004)CrossRefGoogle Scholar
  3. Alves, R., Antunes, F., Salvador, A.: Tools for kinetic modeling of biochemical networks. Nature Biotechnology 24, 667–672 (2006)CrossRefGoogle Scholar
  4. Amgoud, L., Cayrol, C., Lagasquie-Schiex, M.-C.: On bipolarity in argumentation frameworks. In: NMR, pp. 1–9 (2004)Google Scholar
  5. Arroyo-Figueroa, G., Sucar, L.: Temporal Bayesian network of events for diagnosis and prediction in dynamic domains. Applied Intelligence 23, 77–86 (2005)CrossRefGoogle Scholar
  6. Bangsø, O., Olesen, K.: Applying object oriented Bayesian networks to large (medical) decision support systems. In: Proceedings of the Eighth Scandinavian Conference on Artificial Intelligence. IOS Press, Amsterdam (2003)Google Scholar
  7. Baudis, M., Cleary, M.: Progenetix.net: an online repository for molecular cytogenetic aberration data. Bioinformatics 17, 1228–1229 (2001)CrossRefGoogle Scholar
  8. Borak, J., Veilleux, S.: Errors of intuitive logic among physicians. Soc. Sci. Med. 16, 1939–1947 (1982)CrossRefGoogle Scholar
  9. Bulashevska, S., Szakacs, O., Brors, B., Eils, R., Kovacs, G.: Pathways of urothelial cancer progression suggested by Bayesian network analysis of allelotyping data. International Journal of Cancer 110, 850–856 (2004)CrossRefGoogle Scholar
  10. Cristofanilli, M., Hayes, D., Budd, G., Ellis, M., Stopeck, A., Reuben, J., Doyle, G., Matera, J., Allard, W., Miller, M., Fritsche, H., Hortobagyi, G., Terstappen, L.: Circulating tumor cells: A novel prognostic factor for newly diagnosed metastatic breast cancer. J. Clin. Oncol. 23, 1420–1430 (2005)CrossRefGoogle Scholar
  11. Dawid, A., Mortera, J., Vicard, P.: Object-oriented Bayesian networks for complex forensic DNA profiling problems. Forensic Science International 169(256), 195–205 (2007)CrossRefGoogle Scholar
  12. Depew, D., Weber, B.: Darwinism evolving: systems dynamics and the genealogy of natural selection. MIT Press, Cambridge (1996)Google Scholar
  13. Fox, J., Parsons, S.: On using arguments for reasoning about actions and values. In: Proc. AAAI Spring Symposium on Qualitative Preferences in Deliberation and Practical Reasoning, Stanford (1997)Google Scholar
  14. Franklin, B.: Collected Letters, Putnam, New York (1887)Google Scholar
  15. Fridlyand, J., Snijders, A., Ylstra, B., Li, H., Olshen, A., Segraves, R., Dairkee, S., Tokuyasu, T., Ljung, B., Jain, A., McLennan, J., Ziegler, J., Chin, K., Devries, S., Feiler, H., Gray, J., Waldman, F., Pinkel, D., Albertson, D.: Breast tumor copy number aberration phenotypes and genomic instability. BMC Cancer 6, 96 (2006)CrossRefGoogle Scholar
  16. Galea, M., Blamey, R., Elston, C., Ellis, I.: The Nottingham Prognostic Index in primary breast cancer. Breast Cancer Research and Treatment 3, 207–219 (1992)CrossRefGoogle Scholar
  17. Gard, R.: Buddhism. George Braziller, Inc., New York (1961)Google Scholar
  18. Holland, J.: Hidden order: how adaptation builds complexity. Helix Books, New York (1995)Google Scholar
  19. Holland, J.: Emergence: from chaos to order. Addison-Wesley, Redwood City (1998)MATHGoogle Scholar
  20. Hunter, A., Besnard, P.: A logic-based theory of deductive arguments. Artificial Intelligence 128, 203–235 (2001)MATHCrossRefMathSciNetGoogle Scholar
  21. Jaynes, E.T.: Information theory and statistical mechanics. The Physical Review 106(4), 620–630 (1957)CrossRefMathSciNetGoogle Scholar
  22. Kahneman, D., Tversky, A.: On the psychology of prediction. Psychol. Rev. 80, 237–251 (1973)CrossRefGoogle Scholar
  23. Khalil, I., Hill, C.: Systems biology for cancer. Curr. Opin. Oncol. 17, 44–48 (2005)CrossRefGoogle Scholar
  24. Kitano, H.: Biological robustness. Nat. Rev. Genet. 5, 826–837 (2004)CrossRefGoogle Scholar
  25. Koller, D., Pfeffer, A.: Object-oriented Bayesian networks. In: Geiger, D., Shenoy, P. (eds.) Proceedings of the 13th Annual Conference on Uncertainty in Atificial Intelligence, pp. 302–313. Morgan Kaufmann Publishers, San Francisco (1997)Google Scholar
  26. Korb, K.B., Nicholson, A.E.: Bayesian artificial intelligence. Chapman and Hall/CRC Press, London (2003)Google Scholar
  27. Krause, P., Ambler, S., Elvang-Goranssan, M., Fox, J.: A logic of argumentation for reasoning under uncertainty. Computational Intelligence 11, 113–131 (1995)CrossRefMathSciNetGoogle Scholar
  28. Laskey, K., Mahoney, S.: Network fragments: Representing knowledge for constructing probabilistic models. In: Geiger, D., Shenoy, P. (eds.) Proceedings of the 13th Annual Conference on Uncertainty in Artificial Intelligence, pp. 334–341. Morgan Kaufmann Publishers, San Francisco (1997)Google Scholar
  29. Lupski, J., Stankiewicz, P.: Genomic disorders: The genomic basis of disease. Humana Press, Totowa (2006)Google Scholar
  30. Mao, B., Wu, W., Davidson, G., Marhold, J., Li, M., Mechler, B., Delius, H., Hoppe, D., Stannek, P., Walter, C., Glinka, A., Niehrs, C.: Kremen proteins are Dickkopf receptors that regulate Wnt/beta-catenin signalling. Nature 417, 664–667 (2002)CrossRefGoogle Scholar
  31. McPherson, K., Steel, C., Dixon, J.: Breast cancer: Epidemiology, risk factors and genetics. BMJ 321, 624–628 (2000)CrossRefGoogle Scholar
  32. Michielsa, S., Koscielnya, S., Hill, C.: Prediction of cancer outcome with microarrays: a multiple random validation strategy. The Lancet 365(9458), 488–492 (2005)CrossRefGoogle Scholar
  33. Mitchell, S.: Biological complexity and integrative pluralism. Cambrige University Press, Cambridge (2003)Google Scholar
  34. Nagl, S.: Objective Bayesian approaches to biological complexity in cancer. In: Williamson, J. (eds.) Proceedings of the Second Workshop on Combining Probability and Logic. (2005) http://www.kent.ac.uk/sec1/philosophy/jw/2005/progic/
  35. Nagl, S.: A path to knowledge: from data to complex systems models of cancer. In: Nagl, S. (ed.) Cancer Bioinformatics, pp. 3–27. John Wiley & Sons, London (2006)Google Scholar
  36. Nagl, S., Williams, M., El-Mehidi, N., Patkar, V., Williamson, J.: Objective Bayesian nets for integrating cancer knowledge: a systems biology approach. In: Rouso, J., Kaski, S., Ukkonen, E. (eds.) Proceedings of the Workshop on Probabilistic Modelling and Machine Learning in Structural and Systems Biology, Tuusula, June 17–18 2006, vol. B-2006-4, pp. 44–49. Helsinki University Printing House, Finland (2006)Google Scholar
  37. Neapolitan, R.E.: Probabilistic reasoning in expert systems: theory and algorithms. Wiley, New York (1990)Google Scholar
  38. Neapolitan, R.E.: Learning Bayesian networks. Pearson/Prentice Hall, Upper Saddle River (2003)Google Scholar
  39. Nygren, P., Larsson, R.: Overview of the clinical efficacy of investigational anticancer drugs. Journal of Internal Medicine 253, 46–75 (2003)CrossRefGoogle Scholar
  40. Oyama, S.: The ontogeny of information: developmental systems and evolution, 2nd edn. Duke University Press, Durham (2000)Google Scholar
  41. Parsons, S.: Order of magnitude reasoning and qualitative probability. International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems 11(3), 373–390 (2003)MATHCrossRefMathSciNetGoogle Scholar
  42. Parsons, S.: On precise and correct qualitative probabilistic reasoning. International Journal of Approximate Reasoning 35, 111–135 (2004)MATHCrossRefMathSciNetGoogle Scholar
  43. Prakken, H., Sartor, G.: Argument-based extended logic programming with defeasible priorities. In: Schobbens, P.-Y. (ed.) Working Notes of 3rd Model Age Workshop: Formal Models of Agents, Sesimbra, Portugal (1996)Google Scholar
  44. Quinn, M., Allen, E.: Changes in incidence of and mortality from breast cancer in England and Wales since introduction of screening. BMJ 311, 1391–1395 (1995)Google Scholar
  45. Rasnick, D., Duesberg, P.: How aneuploidy affects metabolic control and causes cancer. Biochemical Journal 340, 621–630 (1999)CrossRefGoogle Scholar
  46. Ravdin, Siminoff, Davis.: A computer program to assist in making decisions about adjuvant therapy for women with early breast cancer. J. Clin. Oncol. 19, 980–991 (2001)Google Scholar
  47. Reis-Filho, J., Simpson, P., Gale, T., Lakhan, S.: The molecular genetics of breast cancer: the contribution of comparative genomic hybridization. Pathol. Res. Pract. 201, 713–725 (2005)CrossRefGoogle Scholar
  48. Richards, M., Smith, I., Dixon, J.: Role of systemic treatment for primary operable breast cancer. BMJ 309, 1263–1366 (1994)Google Scholar
  49. Ries, L., Eisner, M., Kosary, C., Hankey, B., Miller, B., Clegg, L., Mariotto, A., Feuer, E., Edwards, B.: SEER Cancer Statistics Review 1975–2001. National Cancer Institute (2004)Google Scholar
  50. Russo, F., Williamson, J.: Interpreting probability in causal models for cancer. In: Russo, F., Williamson, J. (eds.) Causality and probability in the sciences. Texts in Philosophy, pp. 217–241. College Publications, London (2007)Google Scholar
  51. Toyoda, T., Wada, A.: ‘omic space’: coordinate-based integration and analysis of genomic phenomic interactions. Bioinformatics 20, 1759–1765 (2004)CrossRefGoogle Scholar
  52. Veer, L., Paik, S., Hayes, D.: Gene expression profiling of breast cancer: a new tumor marker. J. Clin. Oncol. 23, 1631–1635 (2005)CrossRefGoogle Scholar
  53. Vogelstein, B., Kinzler, K.: Cancer genes and the pathways they control. Nature Medicine 10, 789–799 (2004)CrossRefGoogle Scholar
  54. Williams, M., Williamson, J.: Combining argumentation and Bayesian nets for breast cancer prognosis. Journal of Logic, Language and Information 15, 155–178 (2006)MATHCrossRefMathSciNetGoogle Scholar
  55. Williamson, J.: Maximising entropy efficiently. Electronic Transactions in Artificial Intelligence Journal, 6 (2002), http://www.etaij.org
  56. Williamson, J.: Bayesian nets and causality: philosophical and computational foundations. Oxford University Press, Oxford (2005a)MATHGoogle Scholar
  57. Williamson, J.: Objective Bayesian nets. In: Artemov, S., Barringer, H., ďAvila Garcez, A.S., Lamb, L.C., Woods, J. (eds.) We Will Show Them! Essays in Honour of Dov Gabbay, vol. 2, pp. 713–730. College Publications, London (2005b)Google Scholar
  58. Williamson, J.: Causality. In: Gabbay, D., Guenthner, F. (eds.) Handbook of Philosophical Logic, vol. 14, pp. 89–120. Springer, Heidelberg (2007a)Google Scholar
  59. Williamson, J.: Motivating objective Bayesianism: from empirical constraints to objective probabilities. In: Harper, W.L., Wheeler, G.R. (eds.) Probability and Inference: Essays in Honour of Henry E. Kyburg Jr., pp. 151–179. College Publications, London (2007b)Google Scholar
  60. Williamson, J., Gabbay, D.: Recursive causality in Bayesian networks and self-fibring networks. In: Gillies, D. (ed.) Laws and models in the sciences, pp. 173–221. With comments, pp. 223–245. King’s College Publications, London (2005)Google Scholar
  61. Xia, Y., Yu, H., Jansen, R., Seringhaus, M., Baxter, S., Greenbaum, D., Zhao, H., Gerstein, M.: Analyzing cellular biochemistry in terms of molecular networks. Annu. Rev. Biochem. 73, 1051–1087 (2004)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • Sylvia Nagl
    • 1
  • Matt Williams
    • 2
    • 3
  • Jon Williamson
    • 4
  1. 1.Department of OncologyUniversity College LondonLondonUK
  2. 2.Advanced Computation LaboratoryCancer Research UKUK
  3. 3.Computer ScienceUniversity College LondonLondonUK
  4. 4.Department of PhilosophyUniversity of KentKentUK

Personalised recommendations